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Merge remote-tracking branch 'origin' into feature-multioutput

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This commit is contained in:
Eero Siivola 2018-01-23 15:21:45 +02:00
commit 3bb905be85
4 changed files with 79 additions and 18 deletions
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63
GPy/testing/model_tests.py
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@ -259,29 +259,18 @@ class MiscTests(unittest.TestCase):
np.testing.assert_equal(m.log_likelihood(), m2.log_likelihood())
def test_missing_data(self):
from GPy import kern
from GPy.models.bayesian_gplvm_minibatch import BayesianGPLVMMiniBatch
from GPy.examples.dimensionality_reduction import _simulate_matern
Q = 4
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 400, 3, 4
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]
inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data
Ymissing = Y.copy()
Ymissing[inan] = np.nan
k = kern.Linear(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
k = GPy.kern.Linear(Q, ARD=True) + GPy.kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m = _create_missing_data_model(k, Q)
assert(m.checkgrad())
mul, varl = m.predict(m.X)
k = kern.RBF(Q, ARD=True) + kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m2 = BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=k, missing_data=True)
k = GPy.kern.RBF(Q, ARD=True) + GPy.kern.White(Q, np.exp(-2)) # + kern.bias(Q)
m2 = _create_missing_data_model(k, Q)
assert(m.checkgrad())
m2.kern.rbf.lengthscale[:] = 1e6
m2.X[:] = m.X.param_array
m2.likelihood[:] = m.likelihood[:]
m2.kern.white[:] = m.kern.white[:]
@ -1082,6 +1071,46 @@ class GradientTests(np.testing.TestCase):
m.randomize()
self.assertTrue(m.checkgrad())
def test_posterior_covariance(self):
k = GPy.kern.Poly(2, order=1)
X1 = np.array([
[-2, 2],
[-1, 1]
])
X2 = np.array([
[2, 3],
[-1, 3]
])
Y = np.array([[1], [2]])
m = GPy.models.GPRegression(X1, Y, kernel=k)
result = m.posterior_covariance_between_points(X1, X2)
expected = np.array([[0.4, 2.2], [1.0, 1.0]]) / 3.0
self.assertTrue(np.allclose(result, expected))
def test_posterior_covariance_missing_data(self):
Q = 4
k = GPy.kern.Linear(Q, ARD=True)
m = _create_missing_data_model(k, Q)
with self.assertRaises(RuntimeError):
m.posterior_covariance_between_points(np.array([[1], [2]]), np.array([[3], [4]]))
def _create_missing_data_model(kernel, Q):
D1, D2, D3, N, num_inducing = 13, 5, 8, 400, 3
_, _, Ylist = GPy.examples.dimensionality_reduction._simulate_matern(D1, D2, D3, N, num_inducing, False)
Y = Ylist[0]
inan = np.random.binomial(1, .9, size=Y.shape).astype(bool) # 80% missing data
Ymissing = Y.copy()
Ymissing[inan] = np.nan
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(Ymissing, Q, init="random", num_inducing=num_inducing,
kernel=kernel, missing_data=True)
return m
if __name__ == "__main__":
print("Running unit tests, please be (very) patient...")
unittest.main()